As input for the first step of training the document vectors, all
articles that mention any of the target banks are used. Corresponding
document vectors are learned for each of the 262k articles, trained
over sequences of their in total 210M words, that capture the semantics
specifically of reporting related to our banks of interest. The train
set could be expanded to include other text, too, as more training
data (often up to several billion words) generally provide semantic
models with higher accuracy and broader coverage. However, it is not
clear that a broader coverage in training data would benefit the current
task, while it would substantially increase the computational time
and space complexities. In practice, the memory required to train
on large numbers of documents, e.g., exceeding a million, is likely
to be a bottleneck on standard PC hardware. We train a vector of length
400 using a context of 8 words (similar to the sentiment experiment
of [1]}).
